Deep generative models have shown promising results in generating realistic
images, but it is still non-trivial to generate images with complicated
structures. The main reason is that most of the current generative models fail
to explore the structures in the images including spatial layout and semantic
relations between objects. To address this issue, we propose a novel deep
structured generative model which boosts generative adversarial networks (GANs)
with the aid of structure information. In particular, the layout or structure
of the scene is encoded by a stochastic and-or graph (sAOG), in which the
terminal nodes represent single objects and edges represent relations between
objects. With the sAOG appropriately harnessed, our model can successfully
capture the intrinsic structure in the scenes and generate images of
complicated scenes accordingly. Furthermore, a detection network is introduced
to infer scene structures from a image. Experimental results demonstrate the
effectiveness of our proposed method on both modeling the intrinsic structures,
and generating realistic images.

本文提出了一种新颖的深度结构生成模型，它基于随机与或图来编码场景的结构，并引入了检测网络来推断图像中的场景结构。实验结果表明，该方法不仅能够建模内在结构，还可以生成逼真的图像。

深度结构生成模型

Deep Structured Generative Models

Dimensionality reduction techniques play important roles in the analysis of
big data. Traditional dimensionality reduction approaches, such as principal
component analysis (PCA) and linear discriminant analysis (LDA), have been
studied extensively in the past few decades. However, as the dimensionality of
data increases, the computational cost of traditional dimensionality reduction
methods grows exponentially, and the computation becomes prohibitively
intractable. These drawbacks have triggered the development of random
projection (RP) techniques, which map high-dimensional data onto a
low-dimensional subspace with extremely reduced time cost. However, the RP
transformation matrix is generated without considering the intrinsic structure
of the original data and usually leads to relatively high distortion.
Therefore, in recent years, methods based on RP have been proposed to address
this problem. In this paper, we summarize the methods used in different
situations to help practitioners to employ the proper techniques for their
specific applications. Meanwhile, we enumerate the benefits and limitations of
the various methods and provide further references for researchers to develop
novel RP-based approaches.

本文总结了基于随机投影的不同方法的使用情况，旨在帮助实践者为其特定应用选择合适的技术，并列举了各种方法的优缺点，并为研究人员提供进一步的参考，以开发新的基于随机投影的方法。